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How to Solve Context Propagation Challenges in Distributed Tracing

Distributed tracing allows tracking of a specific flow as it progresses throughout microservices systems. With distributed tracing, developers can connect the dots and gain visibility and understanding of a certain flow. This saves time when building and operating modern applications.

However, not all workflows and architectures support distributed tracing mechanisms. This is the case especially when these mechanisms were built as creative and customized solutions to unique situations. In such cases, it can be challenging to implement the context propagation mechanism, which is responsible for gathering the metadata that enables building the distributed tracing flow.

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Distributed Tracing in ASP.NET Core With Jaeger and Tye, Part 2: Project Tye

In This Series:

  1. Distributed Tracing With Jaeger
  2. Simplifying the Setup With Tye (this article)

Tye is an experimental dotnet tool from Microsoft that aims to make developing, testing, and deploying microservices easier. Tye's opinionated nature greatly simplifies the lifecycle of development and deployment of .NET Core microservices.

To understand the benefits of Tye, let's enumerate the steps involved in the development and deployment of the DCalculator application to Kubernetes:

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Distributed Tracing in ASP.NET Core With Jaeger and Tye Part 1: Distributed Tracing

In This Series:

  1. Distributed Tracing With Jaeger (this article)
  2. Simplifying the Setup With Tye (coming soon)

Modern microservices applications consist of many services deployed on various hosts such as Kubernetes, AWS ECS, and Azure App Services or serverless compute services such as AWS Lambda and Azure Functions. One of the key challenges of microservices is the reduced visibility of requests that span multiple services. In distributed systems that perform various operations such as database queries, publish and consume messages, and trigger jobs, how would you quickly find issues and monitor the behavior of services? The answer to the perplexing problem is Distributed Tracing.

Distributed Tracing, Open Tracing, and Jaeger

Distributed Tracing is the capability of a tracing solution that you can use to track a request across multiple services. The tracing solutions use one or more correlation IDs to collate the request traces and store the traces, which are structured log events across different services, in a central database.

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Microservices Implementation using (Spring Boot and Cloud)

In recent years, microservices architecture has been a preferred choice for application development due to various advantage that architecture brings in. However, every architecture comes with various pain areas and microservices architecture is no different. In a microservices architecture, there are many independently developed services which will serve some specific functionality. These services will be deployed in different -2 Infrastructure and they would communicate with each other and some external system through API or Events to complete the system functionality. We should minimize this kind of direct dependencies on other microservices, but in some cases, it is unavoidable. So we need to managed these dependencies and this is where the microservices architecture principle comes into the picture and explains, what all concerns (Service Discovery, Circuit Breaker, Distributed Tracing, routing, Connector, Configurations) need to be considered while developing and deploying these small services. 

So, here I am trying to depict the diagram, how can microservices be built using Spring boot and be deployed and managed using Spring Cloud. 

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Spring Cloud and Spring Boot, Part 2: Implementing Zipkin Server For Distributed Tracing

In my last article you have learn how to implement Eureka Server for service discovery and registration. In this article, you will learn one more important feature of microservices, Distributed Tracing.

What is Distributed Tracing?

Distributed Tracing is crucial for troubleshooting and understanding microservices. It is very useful when we need to track the request passing through multiple microservices. Distributed Tracing can be used to measure the performance of the microservices.